1. Field of the Invention
The present invention relates to an information processing apparatus, an operation supporting method, and a computer program product for managing electronic files by using user's operation history.
2. Description of the Related Art
Various operations can be assumed to be performed onto electronic files in computers. Examples of such various operations can include those targeted for an electronic file, such as copying and moving an electronic file among recording media in the same computer or among computers, and deleting an electronic file itself. Such operations targeted for an electronic file can also include those performed on an application, such as an operation of transmitting an electronic file as being attached to an electronic mail and an operation of calling an electronic file from a specific application for printing. In addition, the operations can include those performed by an application onto an electronic file, such as editing by a document editor and compression of an electronic file for reducing the file capacity.
Furthermore, even for an operation of printing an electronic document, conditions have to be set, and various functions have to be selected or designated, such as how many prints are to be produced, whether to print on both sides of a page, and whether to print in full color or monochrome.
Although such functional diversity is preferable in terms of functional expansion, in view of having to select and set many functions and conditions, a user is forced to perform more bothersome operations. For example, even simply selecting duplex printing requires many clicking operations, such as opening a print wizard, clicking a print setting button to cause a setting wizard to be displayed, clicking duplex printing, and then ending the setting wizard. Moreover, the user is required to physically move a pointer device, such as a mouse, for clicking each button. Such bothersome selecting operation may become a barrier to effective usage of the functions.
A method of predicting and presenting functions with a high possibility of being selected by the user has been suggested. For example, Japanese Patent Application Laid-Open No. H10-027089 discloses an operation supporting apparatus including a command history storage unit and a command predicting unit that selects the latest command from out of commands satisfying a predetermined condition as a predicted command. Also, Japanese Patent Application Laid-Open No. H07-306847 discloses a computer operation supporting apparatus that records history of commands launched into a computer system in time series and selects the latest command from out of commands satisfying a predetermined condition as a predicted command. In the patent documents, a method of predicting a user's operation based on previous print history of that user is adopted. Furthermore, for prediction for a specific user or user group, a technique of configuring a user model representing knowledge of that user or user group and performing estimation based on that model is used, which is utilized for operation supporting.
The conventional technique has a problem, however, such that if previous history is not sufficiently accumulated, an accurate model cannot be configured. To get around this problem, a collaborative method of combing information about a plurality of users is often used together. One useful method as this collaborative method is collaborative filtering, which is a technique, for example, in Internet shopping, where a combination of evaluations of products by a plurality of users is obtained as history, thereby predicting an evaluation of a product whose evaluation value has not yet been obtained for a user.
Collaborative filtering is a technique assuming that there is a correlation among evaluations, and when applied to function prediction, a function selection is predicted only from the correlation among the evaluations for each function. However, which function the user will use can depend to a large degree on the electronic file for which the function is to be selected. That is, with information about a target for which the function is to be performed (for example, information about a creator, details, and file name of the electronic file) being taken as input variables and with functions (for example, details of operations to be performed onto the electronic file) being taken as output variables, selection of a function to be performed onto an electronic file on a personal computer depends to a large degree on that target electronic file. As such, for function prediction, a relation between the output variable and the input variables is important. Also, when a plurality of functions are combined for use, such as “combination printing” and “10 prints” in file printing, a dependency between these functions is also important. Such complex dependency between the variables, in particular, the input variables and the output variables and between the output variables, cannot be handled by collaborative filtering.
One method capable of handling such complex dependency between the input and output variables is a Bayesian network. By using this method, such complex dependency can be modelized under an appropriate constraint without discriminating between inputs and outputs. With this, a function, that is, a user's intention, can be predicted with higher accuracy. This method, however, includes few collaborative methods, and these methods cannot be easily applied to function prediction in the present invention. For example, A. Jameson and F. Wittig, “Leveraging data about users in general in the learning of individual user model”, In B. Nebel, editor, Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pages 1185-1192, San Francisco, Calif., 2001, Morgan Kaufman, discloses several methods for how to combine an individual model and a general model. Also, A. Niculescu-Mizil and R. Caruana, “Inductive transfer for Bayesian network structure learning”, In Proc. 11th International Conf. on AI and Statistics, 2007, discloses a method of simultaneously finding a plurality of models with a similar structure, but merely discloses finding a model structure, and the combination technique lacks logical adequacy. Therefore, applicability other than the case applied in the document is not clear, and the method cannot be directly applied to a user-model configuring method usable for general intension estimation of operations of an image forming apparatus.